Evolution of Pavement Winter Roughness
Bibliographic record
Abstract
The functional service level of roads is quantified in terms of roughness. This parameter considers every road surface defect that causes passenger vehicle discomfort. Roughness is measured by a quality index, the international roughness index (IRI). Roughness gives an overall appreciation of road profile quality without, however, permitting a deeper analysis. The overall value of the IRI does not discriminate between the two main factors responsible for winter deterioration of roughness: the subgrade differential heave and crack heaving (winter tenting). Differential heave is the result of variability in frost susceptibility of subgrade. This phenomenon can be detected by isolating the long wavelengths produced at the road surface from the longitudinal profile. Crack heaving is a superficial phenomenon greatly influenced by the application of deicing salts. By isolating the short wavelengths from the profile, it is possible to highlight the influence of this phenomenon on deterioration. The goal of this research is to establish, with the use of a filtering technique of road profile, the contribution of these two main factors to winter deterioration of roughness on five road sections in the Quebec City, Canada, area. This study will then allow for the development of a tool to determine the dominant factor for longitudinal profile deterioration and therefore the use of the best technique to rehabilitate roads.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".